Your ML pipeline might be green, but your model could be failing silently in production due to data issues. Data observability helps you catch these problems.
Observing AI/ML systems in production goes beyond traditional infrastructure metrics. It needs deep insights into data quality, model behavior, and performance to catch silent failures.
Deploying machine learning models to production brings unique challenges. Kubernetes offers powerful tools for managing these complex workflows, but it's not a silver bullet.
Sharing GPUs for AI inference across multiple users or services is tricky. This post explores how to allocate these expensive resources efficiently without sacrificing performance or breaking the bank.